What did I learn today?

• A nice idea for using random matrices in portfolio optimization. In classical Markowitz-style portfolio optimization you’re trying to get the best return for a given risk or the lowest risk for a given return. You can use Lagrange multipliers and just solve this optimization problem, using the correlation matrix you get from the set of assets you have in the portfolio. This optimization process picks out the eigenvectors with the smallest eigenvalues to weight the most in assembling an “optimal” portfolio (trying to have the smallest variance). However, the smallest eigenvalues tend to correspond to noise. So one suggested method is to compare your correlation matrix with a random matrix, and replace everything in the “noise band” with a scaled identity component. This apparently improves the risk estimate substantially (or decreases the underestimate of the risk, perhaps). Original paper here.
• Reading Robinson’s paper on an equivariant Pieri rule, and just trying to do calculations. Learned something about the pattern of the calculations, but not yet sure how that will help.
• Learned that kale fried in duck fat is pretty tasty. Definitely worth remembering.
• Learned that I cannot at this point follow double kettlebell cleans and squats with multiple presses without a break. I can do the presses alone, but not right after swings, cleans, and squats with no break.

What did I learn yesterday?

• A large carb-heavy lunch makes me fall asleep in the afternoon. And maybe too much Roquefort doesn’t help.
• That sleepiness does not lend itself to learning or accomplishing other work.